Large, web-based applications, like e-commerce applications are typically executed and hosted by data-centers that provide the required execution infrastructure like host computers, processes running on those host computers and interconnecting computer networks. Such data-centers are often used by a set of different applications, partially or fully sharing the same execution infrastructure. In addition, this infrastructure may also be used to perform cyclic tasks that are not directly related to application execution, like e.g. backup jobs.
In such a situation, where different applications and background jobs compete for the same shared resources like CPU cycles or main memory of a computer system, monitoring systems that only provide transaction execution tracing and monitoring data may be able to identify and report performance degradations related to transaction executions, but they fail to provide data that allows to identify non-transaction based root causes of transaction performance degradation.
Infrastructure monitoring systems that provide measurement data describing load and utilization of execution infrastructure like host computing systems or processes only allow a coarse correlation of detected infrastructure problems with affected applications, e.g. in situations with hard-wired assignment of hosts or processes to specific applications. In more dynamic setups, where e.g. allocation of processing infrastructure is based on the current application load, the identification of applications or transactions affected by detected processing infrastructure problems based on infrastructure monitoring data alone is not possible.
The identification of non-transactional root causes of detected transaction performance degradations requires manual identification of processing infrastructure involved in the execution of the transactions showing performance degradations, followed by an also manual timing analysis, which compares the time frame of the detected transaction degradation with the load and utilization situation of involved hosts or processes during transaction execution time.
To identify the applications and or transactions affected by an identified host or process infrastructure problem, a manual analysis of execution timing and location of monitored transaction is required to identify those monitored transactions or applications that used the specific processing infrastructure during the time when the infrastructure problem persisted.
Due to the manual nature of the described correlation processes, the accuracy of the provided results is often insufficient and the processes require time consuming and often cumbersome human intervention.
Consequently, a method and system is required that integrates transaction oriented with infrastructure performance monitoring, and that allows a free and frictionless switch between those two aspects. This section provides background information related to the present disclosure which is not necessarily prior art.